The Future Of Generative AI Beyond ChatGPT

The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society. ChatGPT and other tools like it are trained on large amounts of publicly available data.

Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies.

The road to human-level performance just got shorter

Morgan Stanley, for example, is working with OpenAI’s GPT-3 to fine-tune training on wealth management content, so that financial advisors can both search for existing knowledge within the firm and create tailored content for clients easily. It seems likely that users of such systems will need training or assistance in creating effective prompts, and that the knowledge outputs of the LLMs might still need editing or review before being applied. Assuming that such issues are addressed, however, LLMs could rekindle the field of knowledge management and allow it to scale much more effectively. They potentially offer greater levels of understanding of conversation and context awareness than current conversational technologies.

Some of the most remarkable applications of generative AI are in art, music and natural language processing. Clio’s Watson expects this will drive a need to learn prompt engineering skills to produce better content. He expects many firms will improve UX through tools for prompt-based creation; however, IT decision-makers must safeguard corporate data and information while using these tools. Creativity has always been a critical pre-requisite to any company’s innovation process and hence competitiveness.

Factors for retail and CPG organizations to consider

In fact, it is likely that humans should retain the ability to make significant leaps of creativity, even if algorithmic capabilities improve incrementally. Today, most businesses recognize the importance of adopting AI to promote the efficiency and performance of its human workforce. For example, AI is being used to augment health care professionals’ job performance in high-stakes work, advising physicians during surgery and using it as a tool in cancer screenings. And robotics is used to make warehouses run with greater speed and reliability, as well as reducing costs. Enabled by new digital channels, independent writers, podcasters, artists, and musicians can connect with audiences directly to make their own incomes.

And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value. The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions. Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue.

In a tighter labor market, workers have been moving into new roles, accelerating occupational shifts

In some cases, companies are developing custom generative AI model applications by fine-tuning them with proprietary data. The US labor market has been remarkably resilient in the face of recent challenges and rapid genrative ai changes. That kind of adaptability is exactly what it will take to navigate the next chapter as well, supporting individuals while helping businesses meet their talent needs so they can continue driving growth.

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The challenge with validation is you need a performance criterion to regress against and say, “What’s the difference? ” In some cases, that means figuring out how to get that criterion out of a data lake without encroaching on other people’s proprietary performance data. If you say, “Well, we’re only going genrative ai to use our data as the employer,” then you are only basing the criterion off people you’ve already hired. “I actually see AI as being likely to empower dentists to be better diagnosticians and to be able to provide preventative care and monitoring better with such support systems in place,” he says.

For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design.

Next-generation language models – beyond GPT-4 – will understand factors like psychology and the human creative process in more depth, enabling them to create written copy that’s deeper and more engaging. We will also see models iterating on the progress made by tools such as AutoGPT, which enable text-based generative AI applications to create their own prompts, allowing them to carry out more complex tasks. While generative AI is becoming a boon today for image production, restoration of movies, and 3D environment creation, the technology will soon have a significant impact on several other industry verticals. By empowering machines to do more than just replace manual labor and take on creative tasks, we will likely see a broader range of use cases and adoption of generative AI across different sectors.

For example, automakers can use generative design to innovate lighter designs — contributing to their goals of making cars more fuel efficient. AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption. Custeau also believes generative AI could improve the ability to simulate large-scale macroeconomic or geopolitical events. The industry is grappling with a stream of events that have created massive supply chain disruptions that have resulted in long-lasting effects on organizations, the economy and the environment.

• ” And it could come back and say, “Well, most people with your skill profile do these things, but some do A, B, C,” with “C” being coding.
• Practically every enterprise app and service is adopting generative AI in some capacity today.
• For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights.
• Starting from the granular tasks, employees leverage their knowledge and expertise to work their way up toward the larger goal.
• We estimate that 11.8 million workers currently in occupations with shrinking demand may need to move into different lines of work by 2030.

An administrative assistant who takes a similar position with another employer has simply switched jobs and is not part of this analysis. If that person becomes an office manager, they have changed occupations within the same category (office support). If they become a computer systems analyst, they have moved into a different occupational category (STEM professionals). Since we are unable to trace exactly how individual workers moved, we use net declines as a broad proxy. In our forward-looking scenario, we refer to people needing to make transitions if demand is projected to decline in their current occupation.

Other forces affecting future labor demand

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Roughly nine million of them may wind up moving into different occupational categories altogether. Considering what has already transpired, that would bring the total number of occupational transitions through the decade’s end to a level almost 25 percent higher than our earlier estimates, creating a more pronounced shift in the mix of jobs across the economy. Labor supply may continue to be constrained, given that one in four Americans will be of retirement age or older by 2030. Without higher participation rates, increased immigration, or meaningful productivity growth, labor shortages could be a lasting issue as the economy and the population grow. Total employment hit an all-time high after the pandemic, with many employers encountering hiring difficulties. As of April 2023, some ten million positions remained vacant; labor force participation had ticked up but was 0.7 percentage point below its prepandemic level.

They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”). Although generative AI is still in the early stages, the potential applications for businesses are significant and wide-ranging. Generative AI can be used to write code, design products, create marketing content and strategies, streamline operations, analyze legal documents, provide customer service via chatbots, and even accelerate scientific discovery. It can be used on its own or with “humans in the loop”; the latter is more likely at present, given its current level of maturity. While most attention was focused on soaring quits rates during the pandemic, something more structural was also occurring. A subset of people did more than change employers; they moved into different occupations altogether.